Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations15222
Missing cells39966
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory176.0 B

Variable types

Numeric7
DateTime3
Categorical9
Text2
Unsupported1

Alerts

Paiement has constant value "Carte prépayée" Constant
Role has constant value "Caissier" Constant
Statut has constant value "Validé" Constant
Client is highly overall correlated with ID_Client and 6 other fieldsHigh correlation
ID_Client is highly overall correlated with Client and 6 other fieldsHigh correlation
ID_Operation is highly overall correlated with Client and 5 other fieldsHigh correlation
ID_Restaurant is highly overall correlated with Client and 6 other fieldsHigh correlation
ID_User is highly overall correlated with Client and 5 other fieldsHigh correlation
Montant_Rgl is highly overall correlated with Montant_VerséHigh correlation
Montant_Versé is highly overall correlated with Montant_RglHigh correlation
Prenom User is highly overall correlated with Client and 5 other fieldsHigh correlation
Restaurant is highly overall correlated with Client and 6 other fieldsHigh correlation
Référence is highly overall correlated with Client and 3 other fieldsHigh correlation
ID_Client is highly imbalanced (63.9%) Imbalance
Client is highly imbalanced (63.9%) Imbalance
Référence has 15123 (99.3%) missing values Missing
ID_Client has 4532 (29.8%) missing values Missing
Client has 4532 (29.8%) missing values Missing
Bénéficiaire_CPP has 554 (3.6%) missing values Missing
Pointage has 15222 (100.0%) missing values Missing
Montant_Rst is highly skewed (γ1 = -45.24081811) Skewed
ID_Règlement has unique values Unique
ID_Operation has unique values Unique
Pointage is an unsupported type, check if it needs cleaning or further analysis Unsupported
Montant_Rst has 15137 (99.4%) zeros Zeros

Reproduction

Analysis started2024-10-23 15:09:44.733242
Analysis finished2024-10-23 15:09:56.669378
Duration11.94 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

ID_Règlement
Real number (ℝ)

Unique 

Distinct15222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304552.63
Minimum250355
Maximum388034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2024-10-23T17:09:56.751230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum250355
5-th percentile254054.1
Q1271202.75
median299481.5
Q3334226.75
95-th percentile382797.8
Maximum388034
Range137679
Interquartile range (IQR)63024

Descriptive statistics

Standard deviation38098.098
Coefficient of variation (CV)0.12509528
Kurtosis-0.79294062
Mean304552.63
Median Absolute Deviation (MAD)30849
Skewness0.45270286
Sum4.6359002 × 109
Variance1.451465 × 109
MonotonicityNot monotonic
2024-10-23T17:09:56.898232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
388034 1
 
< 0.1%
287967 1
 
< 0.1%
287983 1
 
< 0.1%
287994 1
 
< 0.1%
282957 1
 
< 0.1%
282972 1
 
< 0.1%
286068 1
 
< 0.1%
286072 1
 
< 0.1%
286076 1
 
< 0.1%
286079 1
 
< 0.1%
Other values (15212) 15212
99.9%
ValueCountFrequency (%)
250355 1
< 0.1%
250356 1
< 0.1%
250358 1
< 0.1%
250364 1
< 0.1%
250365 1
< 0.1%
250367 1
< 0.1%
250372 1
< 0.1%
250377 1
< 0.1%
250384 1
< 0.1%
250386 1
< 0.1%
ValueCountFrequency (%)
388034 1
< 0.1%
388014 1
< 0.1%
387991 1
< 0.1%
387944 1
< 0.1%
387932 1
< 0.1%
387930 1
< 0.1%
387914 1
< 0.1%
387912 1
< 0.1%
387897 1
< 0.1%
387748 1
< 0.1%

ID_Operation
Real number (ℝ)

High correlation  Unique 

Distinct15222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4159311 × 108
Minimum2.0190157 × 108
Maximum9.0610577 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2024-10-23T17:09:57.058183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190157 × 108
5-th percentile4.0044006 × 108
Q15.0051582 × 108
median7.0087133 × 108
Q37.0089721 × 108
95-th percentile9.0312642 × 108
Maximum9.0610577 × 108
Range7.042042 × 108
Interquartile range (IQR)2.0038139 × 108

Descriptive statistics

Standard deviation1.4827257 × 108
Coefficient of variation (CV)0.23110062
Kurtosis-0.68986602
Mean6.4159311 × 108
Median Absolute Deviation (MAD)35857.5
Skewness-0.11835631
Sum9.7663303 × 1012
Variance2.1984754 × 1016
MonotonicityNot monotonic
2024-10-23T17:09:57.231033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700915738 1
 
< 0.1%
700877295 1
 
< 0.1%
700877305 1
 
< 0.1%
700877312 1
 
< 0.1%
700874995 1
 
< 0.1%
700874999 1
 
< 0.1%
700876410 1
 
< 0.1%
700876413 1
 
< 0.1%
700876415 1
 
< 0.1%
700876417 1
 
< 0.1%
Other values (15212) 15212
99.9%
ValueCountFrequency (%)
201901567 1
< 0.1%
201901569 1
< 0.1%
201901571 1
< 0.1%
201901572 1
< 0.1%
201901573 1
< 0.1%
201901574 1
< 0.1%
400437757 1
< 0.1%
400437758 1
< 0.1%
400437759 1
< 0.1%
400437761 1
< 0.1%
ValueCountFrequency (%)
906105768 1
< 0.1%
906105735 1
< 0.1%
906105731 1
< 0.1%
906105705 1
< 0.1%
906105699 1
< 0.1%
906105698 1
< 0.1%
906105694 1
< 0.1%
906105691 1
< 0.1%
906105690 1
< 0.1%
906105689 1
< 0.1%
Distinct243
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Minimum2024-01-01 00:00:00
Maximum2024-09-04 00:00:00
2024-10-23T17:09:57.369895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:57.519725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct12870
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Minimum2024-10-23 01:09:21
Maximum2024-10-23 23:47:44
2024-10-23T17:09:57.861439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:58.008411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Paiement
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Carte prépayée
15222 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters213108
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarte prépayée
2nd rowCarte prépayée
3rd rowCarte prépayée
4th rowCarte prépayée
5th rowCarte prépayée

Common Values

ValueCountFrequency (%)
Carte prépayée 15222
100.0%

Length

2024-10-23T17:09:58.183509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:09:58.308478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
carte 15222
50.0%
prépayée 15222
50.0%

Most occurring characters

ValueCountFrequency (%)
a 30444
14.3%
é 30444
14.3%
r 30444
14.3%
e 30444
14.3%
p 30444
14.3%
C 15222
7.1%
t 15222
7.1%
15222
7.1%
y 15222
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 213108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 30444
14.3%
é 30444
14.3%
r 30444
14.3%
e 30444
14.3%
p 30444
14.3%
C 15222
7.1%
t 15222
7.1%
15222
7.1%
y 15222
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 213108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 30444
14.3%
é 30444
14.3%
r 30444
14.3%
e 30444
14.3%
p 30444
14.3%
C 15222
7.1%
t 15222
7.1%
15222
7.1%
y 15222
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 213108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 30444
14.3%
é 30444
14.3%
r 30444
14.3%
e 30444
14.3%
p 30444
14.3%
C 15222
7.1%
t 15222
7.1%
15222
7.1%
y 15222
7.1%

Référence
Real number (ℝ)

High correlation  Missing 

Distinct42
Distinct (%)42.4%
Missing15123
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean5.5411145 × 1015
Minimum585
Maximum9.9424452 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2024-10-23T17:09:58.418604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum585
5-th percentile1.1327267 × 1015
Q12.7593463 × 1015
median4.2933584 × 1015
Q39.4323998 × 1015
95-th percentile9.9283249 × 1015
Maximum9.9424452 × 1015
Range9.9424452 × 1015
Interquartile range (IQR)6.6730535 × 1015

Descriptive statistics

Standard deviation3.2404459 × 1015
Coefficient of variation (CV)0.58480039
Kurtosis-1.4408955
Mean5.5411145 × 1015
Median Absolute Deviation (MAD)1.8670205 × 1015
Skewness0.17979743
Sum5.4857034 × 1017
Variance1.050049 × 1031
MonotonicityNot monotonic
2024-10-23T17:09:58.573176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3.751365738 × 101514
 
0.1%
9.432399813 × 101512
 
0.1%
2.426337889 × 101510
 
0.1%
2.759346313 × 10158
 
0.1%
5.798735672 × 10158
 
0.1%
9.928324916 × 10154
 
< 0.1%
9.942445182 × 10154
 
< 0.1%
4.484248832 × 10152
 
< 0.1%
2.863716814 × 10152
 
< 0.1%
2.388218227 × 10152
 
< 0.1%
Other values (32) 33
 
0.2%
(Missing) 15123
99.3%
ValueCountFrequency (%)
585 1
 
< 0.1%
904 1
 
< 0.1%
1246 1
 
< 0.1%
6024 1
 
< 0.1%
6537 1
 
< 0.1%
1.258585224 × 10151
 
< 0.1%
1.461162782 × 10151
 
< 0.1%
2.388218227 × 10152
 
< 0.1%
2.426337889 × 101510
0.1%
2.759346313 × 10158
0.1%
ValueCountFrequency (%)
9.942445182 × 10154
 
< 0.1%
9.928324916 × 10154
 
< 0.1%
9.921822518 × 10151
 
< 0.1%
9.867569317 × 10151
 
< 0.1%
9.854852615 × 10151
 
< 0.1%
9.854337879 × 10151
 
< 0.1%
9.849287978 × 10151
 
< 0.1%
9.694836984 × 10151
 
< 0.1%
9.484145895 × 10151
 
< 0.1%
9.432399813 × 101512
0.1%

Montant_Rgl
Real number (ℝ)

High correlation 

Distinct401
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.954553
Minimum1.5
Maximum2799.528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2024-10-23T17:09:58.743247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3.5
Q110
median24
Q345
95-th percentile85
Maximum2799.528
Range2798.028
Interquartile range (IQR)35

Descriptive statistics

Standard deviation59.042239
Coefficient of variation (CV)1.7388608
Kurtosis657.75272
Mean33.954553
Median Absolute Deviation (MAD)16
Skewness19.732976
Sum516856.2
Variance3485.986
MonotonicityNot monotonic
2024-10-23T17:09:58.909748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 997
 
6.5%
40 728
 
4.8%
15 706
 
4.6%
10 612
 
4.0%
30 543
 
3.6%
8 498
 
3.3%
18 438
 
2.9%
20 421
 
2.8%
45 410
 
2.7%
5 383
 
2.5%
Other values (391) 9486
62.3%
ValueCountFrequency (%)
1.5 116
 
0.8%
2 317
2.1%
2.5 62
 
0.4%
3 200
1.3%
3.5 115
 
0.8%
4 333
2.2%
4.5 152
 
1.0%
5 383
2.5%
5.5 146
 
1.0%
6 203
1.3%
ValueCountFrequency (%)
2799.527996 1
< 0.1%
2373.942528 1
< 0.1%
1800 1
< 0.1%
1589.951992 1
< 0.1%
1574.750015 1
< 0.1%
1380.32 1
< 0.1%
1292.47821 1
< 0.1%
1005 1
< 0.1%
974.9080048 1
< 0.1%
971.57201 1
< 0.1%

Montant_Versé
Real number (ℝ)

High correlation 

Distinct400
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.954553
Minimum1.5
Maximum2799.5281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2024-10-23T17:09:59.073461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile3.5
Q110
median24
Q345
95-th percentile85
Maximum2799.5281
Range2798.0281
Interquartile range (IQR)35

Descriptive statistics

Standard deviation59.04224
Coefficient of variation (CV)1.7388608
Kurtosis657.75276
Mean33.954553
Median Absolute Deviation (MAD)16
Skewness19.732977
Sum516856.2
Variance3485.9861
MonotonicityNot monotonic
2024-10-23T17:09:59.239795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 997
 
6.5%
40 728
 
4.8%
15 706
 
4.6%
10 612
 
4.0%
30 543
 
3.6%
8 498
 
3.3%
18 438
 
2.9%
20 421
 
2.8%
45 410
 
2.7%
5 383
 
2.5%
Other values (390) 9486
62.3%
ValueCountFrequency (%)
1.5 116
 
0.8%
2 317
2.1%
2.5 62
 
0.4%
3 200
1.3%
3.5 115
 
0.8%
4 333
2.2%
4.5 152
 
1.0%
5 383
2.5%
5.5 146
 
1.0%
6 203
1.3%
ValueCountFrequency (%)
2799.528076 1
< 0.1%
2373.942627 1
< 0.1%
1800 1
< 0.1%
1589.952026 1
< 0.1%
1574.75 1
< 0.1%
1380.319946 1
< 0.1%
1292.478271 1
< 0.1%
1005 1
< 0.1%
974.90802 1
< 0.1%
971.5720215 1
< 0.1%

Montant_Rst
Real number (ℝ)

Skewed  Zeros 

Distinct59
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.4065161 × 10-8
Minimum-9.9182125 × 10-5
Maximum5.3405757 × 10-5
Zeros15137
Zeros (%)99.4%
Negative36
Negative (%)0.2%
Memory size119.1 KiB
2024-10-23T17:09:59.407644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.9182125 × 10-5
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5.3405757 × 10-5
Range0.00015258788
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3204659 × 10-6
Coefficient of variation (CV)-93.882029
Kurtosis3499.9075
Mean-1.4065161 × 10-8
Median Absolute Deviation (MAD)0
Skewness-45.240818
Sum-0.00021409989
Variance1.7436302 × 10-12
MonotonicityNot monotonic
2024-10-23T17:09:59.608261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15137
99.4%
4.97379915 × 10-148
 
0.1%
4.618527782 × 10-148
 
0.1%
-3.730349363 × 10-144
 
< 0.1%
-3.97903932 × 10-133
 
< 0.1%
-2.486899575 × 10-142
 
< 0.1%
-7.105427358 × 10-152
 
< 0.1%
-7.629394389 × 10-62
 
< 0.1%
-7.629394986 × 10-62
 
< 0.1%
3.730349363 × 10-142
 
< 0.1%
Other values (49) 52
 
0.3%
ValueCountFrequency (%)
-9.918212481 × 10-51
< 0.1%
-8.010864485 × 10-51
< 0.1%
-6.103515511 × 10-51
< 0.1%
-3.433227744 × 10-51
< 0.1%
-1.525878952 × 10-51
< 0.1%
-1.525878929 × 10-51
< 0.1%
-1.144409202 × 10-51
< 0.1%
-7.629394986 × 10-62
< 0.1%
-7.629394389 × 10-62
< 0.1%
-7.629394247 × 10-61
< 0.1%
ValueCountFrequency (%)
5.34057574 × 10-51
< 0.1%
2.288818371 × 10-51
< 0.1%
2.288818325 × 10-51
< 0.1%
1.525878997 × 10-51
< 0.1%
1.144409225 × 10-51
< 0.1%
7.629394986 × 10-61
< 0.1%
7.629394304 × 10-61
< 0.1%
3.814696811 × 10-61
< 0.1%
2.145767255 × 10-61
< 0.1%
1.907349002 × 10-61
< 0.1%

ID_Client
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing4532
Missing (%)29.8%
Memory size119.1 KiB
CLT10001
9955 
CLT10002
 
735

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters85520
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLT10001
2nd rowCLT10001
3rd rowCLT10001
4th rowCLT10001
5th rowCLT10001

Common Values

ValueCountFrequency (%)
CLT10001 9955
65.4%
CLT10002 735
 
4.8%
(Missing) 4532
29.8%

Length

2024-10-23T17:09:59.776823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:09:59.900516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
clt10001 9955
93.1%
clt10002 735
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 32070
37.5%
1 20645
24.1%
L 10690
 
12.5%
C 10690
 
12.5%
T 10690
 
12.5%
2 735
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85520
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 32070
37.5%
1 20645
24.1%
L 10690
 
12.5%
C 10690
 
12.5%
T 10690
 
12.5%
2 735
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85520
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 32070
37.5%
1 20645
24.1%
L 10690
 
12.5%
C 10690
 
12.5%
T 10690
 
12.5%
2 735
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85520
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 32070
37.5%
1 20645
24.1%
L 10690
 
12.5%
C 10690
 
12.5%
T 10690
 
12.5%
2 735
 
0.9%

Client
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing4532
Missing (%)29.8%
Memory size119.1 KiB
CLIENT AU COMPTANT
9955 
CLIENT CARTE PRÉPAYÉE
 
735

Length

Max length21
Median length18
Mean length18.206268
Min length18

Characters and Unicode

Total characters194625
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLIENT AU COMPTANT
2nd rowCLIENT AU COMPTANT
3rd rowCLIENT AU COMPTANT
4th rowCLIENT AU COMPTANT
5th rowCLIENT AU COMPTANT

Common Values

ValueCountFrequency (%)
CLIENT AU COMPTANT 9955
65.4%
CLIENT CARTE PRÉPAYÉE 735
 
4.8%
(Missing) 4532
29.8%

Length

2024-10-23T17:10:00.048085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:10:00.183040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
client 10690
33.3%
au 9955
31.0%
comptant 9955
31.0%
carte 735
 
2.3%
prépayée 735
 
2.3%

Most occurring characters

ValueCountFrequency (%)
T 31335
16.1%
21380
11.0%
C 21380
11.0%
A 21380
11.0%
N 20645
10.6%
E 12160
 
6.2%
P 11425
 
5.9%
L 10690
 
5.5%
I 10690
 
5.5%
O 9955
 
5.1%
Other values (5) 23585
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 194625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 31335
16.1%
21380
11.0%
C 21380
11.0%
A 21380
11.0%
N 20645
10.6%
E 12160
 
6.2%
P 11425
 
5.9%
L 10690
 
5.5%
I 10690
 
5.5%
O 9955
 
5.1%
Other values (5) 23585
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 194625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 31335
16.1%
21380
11.0%
C 21380
11.0%
A 21380
11.0%
N 20645
10.6%
E 12160
 
6.2%
P 11425
 
5.9%
L 10690
 
5.5%
I 10690
 
5.5%
O 9955
 
5.1%
Other values (5) 23585
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 194625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 31335
16.1%
21380
11.0%
C 21380
11.0%
A 21380
11.0%
N 20645
10.6%
E 12160
 
6.2%
P 11425
 
5.9%
L 10690
 
5.5%
I 10690
 
5.5%
O 9955
 
5.1%
Other values (5) 23585
12.1%
Distinct1007
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
2024-10-23T17:10:00.509346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length16.012416
Min length5

Characters and Unicode

Total characters243741
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)1.4%

Sample

1st row1237736457745921
2nd row9854337878921188
3rd row4284648158394883
4th row3628329735567861
5th row9536648418398212
ValueCountFrequency (%)
4284648158394883 278
 
1.8%
1461162782251889 275
 
1.8%
6632395296331911 249
 
1.6%
2863716813744826 242
 
1.6%
5676118786483647 239
 
1.6%
2426337888522318 227
 
1.5%
2298119326996171 213
 
1.4%
1271645626186163 201
 
1.3%
1443451317787646 197
 
1.3%
1258585224496584 196
 
1.3%
Other values (997) 12905
84.8%
2024-10-23T17:10:01.072330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28792
11.8%
4 28680
11.8%
3 28423
11.7%
2 28229
11.6%
8 28178
11.6%
6 27496
11.3%
9 27268
11.2%
5 23403
9.6%
7 22266
9.1%
0 382
 
0.2%
Other values (11) 624
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28792
11.8%
4 28680
11.8%
3 28423
11.7%
2 28229
11.6%
8 28178
11.6%
6 27496
11.3%
9 27268
11.2%
5 23403
9.6%
7 22266
9.1%
0 382
 
0.2%
Other values (11) 624
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28792
11.8%
4 28680
11.8%
3 28423
11.7%
2 28229
11.6%
8 28178
11.6%
6 27496
11.3%
9 27268
11.2%
5 23403
9.6%
7 22266
9.1%
0 382
 
0.2%
Other values (11) 624
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28792
11.8%
4 28680
11.8%
3 28423
11.7%
2 28229
11.6%
8 28178
11.6%
6 27496
11.3%
9 27268
11.2%
5 23403
9.6%
7 22266
9.1%
0 382
 
0.2%
Other values (11) 624
 
0.3%

Bénéficiaire_CPP
Text

Missing 

Distinct855
Distinct (%)5.8%
Missing554
Missing (%)3.6%
Memory size119.1 KiB
2024-10-23T17:10:01.440063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length8.8797382
Min length2

Characters and Unicode

Total characters130248
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)1.1%

Sample

1st rowMOUMOUN
2nd rowFARAIDI
3rd rowRACHIDI Salah
4th rowEL AZRAK
5th rowZERIOUEL
ValueCountFrequency (%)
el 2031
 
10.0%
mohamed 821
 
4.1%
yao 494
 
2.4%
salah 295
 
1.5%
rachidi 295
 
1.5%
djeth 289
 
1.4%
doudouh 272
 
1.3%
selmani 255
 
1.3%
nait 245
 
1.2%
yazza 244
 
1.2%
Other values (911) 14986
74.1%
2024-10-23T17:10:01.979863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 19759
15.2%
I 11027
 
8.5%
E 10094
 
7.7%
O 8133
 
6.2%
H 7425
 
5.7%
L 6887
 
5.3%
M 6476
 
5.0%
U 6454
 
5.0%
R 5903
 
4.5%
D 5784
 
4.4%
Other values (40) 42306
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 130248
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 19759
15.2%
I 11027
 
8.5%
E 10094
 
7.7%
O 8133
 
6.2%
H 7425
 
5.7%
L 6887
 
5.3%
M 6476
 
5.0%
U 6454
 
5.0%
R 5903
 
4.5%
D 5784
 
4.4%
Other values (40) 42306
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 130248
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 19759
15.2%
I 11027
 
8.5%
E 10094
 
7.7%
O 8133
 
6.2%
H 7425
 
5.7%
L 6887
 
5.3%
M 6476
 
5.0%
U 6454
 
5.0%
R 5903
 
4.5%
D 5784
 
4.4%
Other values (40) 42306
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 130248
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 19759
15.2%
I 11027
 
8.5%
E 10094
 
7.7%
O 8133
 
6.2%
H 7425
 
5.7%
L 6887
 
5.3%
M 6476
 
5.0%
U 6454
 
5.0%
R 5903
 
4.5%
D 5784
 
4.4%
Other values (40) 42306
32.5%

Solde_CPP
Real number (ℝ)

Distinct5346
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.31823
Minimum1.5
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2024-10-23T17:10:02.159441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile22.5
Q1100
median330
Q3960.875
95-th percentile1904.3
Maximum6000
Range5998.5
Interquartile range (IQR)860.875

Descriptive statistics

Standard deviation671.6558
Coefficient of variation (CV)1.0638942
Kurtosis0.65377392
Mean631.31823
Median Absolute Deviation (MAD)279
Skewness1.1439607
Sum9609926.2
Variance451121.51
MonotonicityNot monotonic
2024-10-23T17:10:02.340926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1903.1 108
 
0.7%
100 102
 
0.7%
1904.1 79
 
0.5%
1903.3 63
 
0.4%
1902.1 61
 
0.4%
1903.11 59
 
0.4%
1902.12 59
 
0.4%
1903.12 58
 
0.4%
1903.9 58
 
0.4%
200 57
 
0.4%
Other values (5336) 14518
95.4%
ValueCountFrequency (%)
1.5 3
 
< 0.1%
2 7
 
< 0.1%
2.5 9
0.1%
3 11
0.1%
3.14 1
 
< 0.1%
3.5 12
0.1%
4 7
 
< 0.1%
4.5 7
 
< 0.1%
5 20
0.1%
5.28 1
 
< 0.1%
ValueCountFrequency (%)
6000 1
< 0.1%
5000 1
< 0.1%
4995 1
< 0.1%
4955 1
< 0.1%
4946 1
< 0.1%
4940 1
< 0.1%
4905 1
< 0.1%
3945.63 1
< 0.1%
3881.57 1
< 0.1%
3406.5 1
< 0.1%

ID_Restaurant
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
RST10005
8152 
RST10003
4639 
RST10012
1111 
RST10010
 
641
RST10014
 
528
Other values (2)
 
151

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters121776
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRST10005
2nd rowRST10005
3rd rowRST10005
4th rowRST10005
5th rowRST10005

Common Values

ValueCountFrequency (%)
RST10005 8152
53.6%
RST10003 4639
30.5%
RST10012 1111
 
7.3%
RST10010 641
 
4.2%
RST10014 528
 
3.5%
RST10006 94
 
0.6%
RST10004 57
 
0.4%

Length

2024-10-23T17:10:02.508342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:10:02.656310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rst10005 8152
53.6%
rst10003 4639
30.5%
rst10012 1111
 
7.3%
rst10010 641
 
4.2%
rst10014 528
 
3.5%
rst10006 94
 
0.6%
rst10004 57
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 44027
36.2%
1 17502
 
14.4%
S 15222
 
12.5%
R 15222
 
12.5%
T 15222
 
12.5%
5 8152
 
6.7%
3 4639
 
3.8%
2 1111
 
0.9%
4 585
 
0.5%
6 94
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 44027
36.2%
1 17502
 
14.4%
S 15222
 
12.5%
R 15222
 
12.5%
T 15222
 
12.5%
5 8152
 
6.7%
3 4639
 
3.8%
2 1111
 
0.9%
4 585
 
0.5%
6 94
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 44027
36.2%
1 17502
 
14.4%
S 15222
 
12.5%
R 15222
 
12.5%
T 15222
 
12.5%
5 8152
 
6.7%
3 4639
 
3.8%
2 1111
 
0.9%
4 585
 
0.5%
6 94
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 44027
36.2%
1 17502
 
14.4%
S 15222
 
12.5%
R 15222
 
12.5%
T 15222
 
12.5%
5 8152
 
6.7%
3 4639
 
3.8%
2 1111
 
0.9%
4 585
 
0.5%
6 94
 
0.1%

Restaurant
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Café - Boulangerie - Pâtisserie - Rabat
8152 
Snack Pizzeria - Rabat
4639 
L'Casis Cafétéria HCZ
1111 
Lavomatic - Rabat
 
641
Epicerie - Rabat
 
528
Other values (2)
 
151

Length

Max length39
Median length39
Mean length30.717908
Min length16

Characters and Unicode

Total characters467588
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCafé - Boulangerie - Pâtisserie - Rabat
2nd rowCafé - Boulangerie - Pâtisserie - Rabat
3rd rowCafé - Boulangerie - Pâtisserie - Rabat
4th rowCafé - Boulangerie - Pâtisserie - Rabat
5th rowCafé - Boulangerie - Pâtisserie - Rabat

Common Values

ValueCountFrequency (%)
Café - Boulangerie - Pâtisserie - Rabat 8152
53.6%
Snack Pizzeria - Rabat 4639
30.5%
L'Casis Cafétéria HCZ 1111
 
7.3%
Lavomatic - Rabat 641
 
4.2%
Epicerie - Rabat 528
 
3.5%
Pharmacie & Parapharmacie - Rabat 94
 
0.6%
Restaurant Gastronomique - Rabat 57
 
0.4%

Length

2024-10-23T17:10:02.796016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:10:02.928547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
30509
36.7%
rabat 14111
17.0%
boulangerie 8152
 
9.8%
café 8152
 
9.8%
pâtisserie 8152
 
9.8%
snack 4639
 
5.6%
pizzeria 4639
 
5.6%
l'casis 1111
 
1.3%
cafétéria 1111
 
1.3%
hcz 1111
 
1.3%
Other values (6) 1471
 
1.8%

Most occurring characters

ValueCountFrequency (%)
67936
14.5%
a 59154
12.7%
e 38605
 
8.3%
i 37898
 
8.1%
- 30415
 
6.5%
t 24186
 
5.2%
r 22978
 
4.9%
s 18640
 
4.0%
R 14168
 
3.0%
b 14111
 
3.0%
Other values (27) 139497
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 467588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
67936
14.5%
a 59154
12.7%
e 38605
 
8.3%
i 37898
 
8.1%
- 30415
 
6.5%
t 24186
 
5.2%
r 22978
 
4.9%
s 18640
 
4.0%
R 14168
 
3.0%
b 14111
 
3.0%
Other values (27) 139497
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 467588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
67936
14.5%
a 59154
12.7%
e 38605
 
8.3%
i 37898
 
8.1%
- 30415
 
6.5%
t 24186
 
5.2%
r 22978
 
4.9%
s 18640
 
4.0%
R 14168
 
3.0%
b 14111
 
3.0%
Other values (27) 139497
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 467588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
67936
14.5%
a 59154
12.7%
e 38605
 
8.3%
i 37898
 
8.1%
- 30415
 
6.5%
t 24186
 
5.2%
r 22978
 
4.9%
s 18640
 
4.0%
R 14168
 
3.0%
b 14111
 
3.0%
Other values (27) 139497
29.8%

ID_User
Categorical

High correlation 

Distinct13
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size119.1 KiB
USR10004
4158 
USR10003
3581 
USR10008
2348 
USR10002
1963 
USR10001
797 
Other values (8)
2374 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters121768
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSR10004
2nd rowUSR10004
3rd rowUSR10004
4th rowUSR10004
5th rowUSR10004

Common Values

ValueCountFrequency (%)
USR10004 4158
27.3%
USR10003 3581
23.5%
USR10008 2348
15.4%
USR10002 1963
12.9%
USR10001 797
 
5.2%
USR10022 641
 
4.2%
USR10023 515
 
3.4%
USR10024 361
 
2.4%
USR10029 324
 
2.1%
USR10027 235
 
1.5%
Other values (3) 298
 
2.0%

Length

2024-10-23T17:10:03.078521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usr10004 4158
27.3%
usr10003 3581
23.5%
usr10008 2348
15.4%
usr10002 1963
12.9%
usr10001 797
 
5.2%
usr10022 641
 
4.2%
usr10023 515
 
3.4%
usr10024 361
 
2.4%
usr10029 324
 
2.1%
usr10027 235
 
1.5%
Other values (3) 298
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 43493
35.7%
1 16018
 
13.2%
S 15221
 
12.5%
U 15221
 
12.5%
R 15221
 
12.5%
2 4774
 
3.9%
4 4519
 
3.7%
3 4300
 
3.5%
8 2416
 
2.0%
9 324
 
0.3%
Other values (2) 261
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 43493
35.7%
1 16018
 
13.2%
S 15221
 
12.5%
U 15221
 
12.5%
R 15221
 
12.5%
2 4774
 
3.9%
4 4519
 
3.7%
3 4300
 
3.5%
8 2416
 
2.0%
9 324
 
0.3%
Other values (2) 261
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 43493
35.7%
1 16018
 
13.2%
S 15221
 
12.5%
U 15221
 
12.5%
R 15221
 
12.5%
2 4774
 
3.9%
4 4519
 
3.7%
3 4300
 
3.5%
8 2416
 
2.0%
9 324
 
0.3%
Other values (2) 261
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 43493
35.7%
1 16018
 
13.2%
S 15221
 
12.5%
U 15221
 
12.5%
R 15221
 
12.5%
2 4774
 
3.9%
4 4519
 
3.7%
3 4300
 
3.5%
8 2416
 
2.0%
9 324
 
0.3%
Other values (2) 261
 
0.2%

Prenom User
Categorical

High correlation 

Distinct12
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size119.1 KiB
Souhail
4311 
Hamza
4158 
Soundoss
3581 
Ayoub
797 
Fadwa
641 
Other values (7)
1733 

Length

Max length10
Median length9
Mean length6.5225018
Min length5

Characters and Unicode

Total characters99279
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHamza
2nd rowHamza
3rd rowHamza
4th rowHamza
5th rowHamza

Common Values

ValueCountFrequency (%)
Souhail 4311
28.3%
Hamza 4158
27.3%
Soundoss 3581
23.5%
Ayoub 797
 
5.2%
Fadwa 641
 
4.2%
Abdelkarim 515
 
3.4%
Morad 361
 
2.4%
Moncef 324
 
2.1%
Ayman 235
 
1.5%
Abdelkrim 204
 
1.3%
Other values (2) 94
 
0.6%

Length

2024-10-23T17:10:03.209053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
souhail 4311
28.3%
hamza 4158
27.3%
soundoss 3581
23.5%
ayoub 797
 
5.2%
fadwa 641
 
4.2%
abdelkarim 515
 
3.4%
morad 361
 
2.4%
moncef 324
 
2.1%
ayman 235
 
1.5%
abdelkrim 204
 
1.3%
Other values (2) 94
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 15140
15.2%
o 13023
13.1%
u 8757
 
8.8%
S 7892
 
7.9%
s 7162
 
7.2%
d 5370
 
5.4%
m 5112
 
5.1%
i 5030
 
5.1%
l 5030
 
5.1%
h 4311
 
4.3%
Other values (16) 22452
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99279
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 15140
15.2%
o 13023
13.1%
u 8757
 
8.8%
S 7892
 
7.9%
s 7162
 
7.2%
d 5370
 
5.4%
m 5112
 
5.1%
i 5030
 
5.1%
l 5030
 
5.1%
h 4311
 
4.3%
Other values (16) 22452
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99279
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 15140
15.2%
o 13023
13.1%
u 8757
 
8.8%
S 7892
 
7.9%
s 7162
 
7.2%
d 5370
 
5.4%
m 5112
 
5.1%
i 5030
 
5.1%
l 5030
 
5.1%
h 4311
 
4.3%
Other values (16) 22452
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99279
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 15140
15.2%
o 13023
13.1%
u 8757
 
8.8%
S 7892
 
7.9%
s 7162
 
7.2%
d 5370
 
5.4%
m 5112
 
5.1%
i 5030
 
5.1%
l 5030
 
5.1%
h 4311
 
4.3%
Other values (16) 22452
22.6%

Role
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size119.1 KiB
Caissier
15221 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters121768
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaissier
2nd rowCaissier
3rd rowCaissier
4th rowCaissier
5th rowCaissier

Common Values

ValueCountFrequency (%)
Caissier 15221
> 99.9%
(Missing) 1
 
< 0.1%

Length

2024-10-23T17:10:03.333531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:10:03.432446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
caissier 15221
100.0%

Most occurring characters

ValueCountFrequency (%)
s 30442
25.0%
i 30442
25.0%
a 15221
12.5%
C 15221
12.5%
e 15221
12.5%
r 15221
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 30442
25.0%
i 30442
25.0%
a 15221
12.5%
C 15221
12.5%
e 15221
12.5%
r 15221
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 30442
25.0%
i 30442
25.0%
a 15221
12.5%
C 15221
12.5%
e 15221
12.5%
r 15221
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 30442
25.0%
i 30442
25.0%
a 15221
12.5%
C 15221
12.5%
e 15221
12.5%
r 15221
12.5%

Statut
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Validé
15222 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters91332
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValidé
2nd rowValidé
3rd rowValidé
4th rowValidé
5th rowValidé

Common Values

ValueCountFrequency (%)
Validé 15222
100.0%

Length

2024-10-23T17:10:03.528322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-23T17:10:03.624011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
validé 15222
100.0%

Most occurring characters

ValueCountFrequency (%)
V 15222
16.7%
a 15222
16.7%
l 15222
16.7%
i 15222
16.7%
d 15222
16.7%
é 15222
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 15222
16.7%
a 15222
16.7%
l 15222
16.7%
i 15222
16.7%
d 15222
16.7%
é 15222
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 15222
16.7%
a 15222
16.7%
l 15222
16.7%
i 15222
16.7%
d 15222
16.7%
é 15222
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 15222
16.7%
a 15222
16.7%
l 15222
16.7%
i 15222
16.7%
d 15222
16.7%
é 15222
16.7%

Pointage
Unsupported

Missing  Rejected  Unsupported 

Missing15222
Missing (%)100.0%
Memory size119.1 KiB
Distinct243
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Minimum2024-01-01 00:00:00
Maximum2024-09-04 00:00:00
2024-10-23T17:10:03.732147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:10:03.865703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-10-23T17:09:55.003290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:48.334475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.291451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.218379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:51.209240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.178322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.089772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:55.118503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:48.508188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.425428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.428497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:52.411216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.320774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.238284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:55.254929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:48.648312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.541817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.558406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:52.560472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.449805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.368171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:55.363763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:48.760962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.641926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.672475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:52.682181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.578440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.491217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:55.478679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:48.888143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.767067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.799337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:52.797395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.693656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.618325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:55.598265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.014821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.883480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.908947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:52.913187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.818047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.739639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:55.721714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:49.158152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:50.039151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:51.047956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.058298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:53.961830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-23T17:09:54.873555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-23T17:10:03.968600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ClientID_ClientID_OperationID_RestaurantID_RèglementID_UserMontant_RglMontant_RstMontant_VerséPrenom UserRestaurantRéférenceSolde_CPP
Client1.0000.9990.8911.0000.1220.9990.1550.1300.1551.0001.0000.9620.093
ID_Client0.9991.0000.8911.0000.1220.9990.1550.1300.1551.0001.0000.9620.093
ID_Operation0.8910.8911.0000.7220.3280.569-0.4050.012-0.4050.5670.7220.266-0.241
ID_Restaurant1.0001.0000.7221.0000.3110.8930.2460.1550.2460.8921.0000.5100.155
ID_Règlement0.1220.1220.3280.3111.0000.2710.0880.0170.0880.2700.3110.2780.098
ID_User0.9990.9990.5690.8930.2711.0000.2030.1690.2031.0000.8930.3690.134
Montant_Rgl0.1550.155-0.4050.2460.0880.2031.0000.0021.0000.2030.2460.1200.305
Montant_Rst0.1300.1300.0120.1550.0170.1690.0021.0000.0020.1690.155-0.1450.008
Montant_Versé0.1550.155-0.4050.2460.0880.2031.0000.0021.0000.2030.2460.1200.305
Prenom User1.0001.0000.5670.8920.2701.0000.2030.1690.2031.0000.8920.4230.134
Restaurant1.0001.0000.7221.0000.3110.8930.2460.1550.2460.8921.0000.5100.155
Référence0.9620.9620.2660.5100.2780.3690.120-0.1450.1200.4230.5101.000-0.058
Solde_CPP0.0930.093-0.2410.1550.0980.1340.3050.0080.3050.1340.155-0.0581.000

Missing values

2024-10-23T17:09:55.918257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-23T17:09:56.293604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-23T17:09:56.552048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ID_RèglementID_OperationDate_RèglementHeure_RèglementPaiementRéférenceMontant_RglMontant_VerséMontant_RstID_ClientClientID_CartePPBénéficiaire_CPPSolde_CPPID_RestaurantRestaurantID_UserPrenom UserRoleStatutPointageDate_Sys
02879677008772952024-02-2908:07:37Carte prépayéeNaN13.513.50.0CLT10001CLIENT AU COMPTANT1237736457745921MOUMOUN952.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-29
12879837008773052024-02-2908:17:54Carte prépayéeNaN8.08.00.0CLT10001CLIENT AU COMPTANT9854337878921188FARAIDI921.51RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-29
22879947008773122024-02-2908:23:33Carte prépayéeNaN20.020.00.0CLT10001CLIENT AU COMPTANT4284648158394883RACHIDI Salah790.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-29
32829577008749952024-02-2211:19:17Carte prépayéeNaN87.087.00.0CLT10001CLIENT AU COMPTANT3628329735567861EL AZRAK346.23RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-22
42829727008749992024-02-2211:31:14Carte prépayéeNaN5.05.00.0CLT10001CLIENT AU COMPTANT9536648418398212ZERIOUEL875.50RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-22
52860687008764102024-02-2708:16:08Carte prépayéeNaN8.08.00.0CLT10001CLIENT AU COMPTANT3459944654116885CHAOUNI BENABDALLAH961.50RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-27
62860727008764132024-02-2708:17:21Carte prépayéeNaN12.012.00.0CLT10001CLIENT AU COMPTANT1468957443591868ZAZARI185.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-27
72860767008764152024-02-2708:17:59Carte prépayéeNaN8.08.00.0CLT10001CLIENT AU COMPTANT3459944654116885CHAOUNI BENABDALLAH953.50RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-27
82860797008764172024-02-2708:19:31Carte prépayéeNaN45.045.00.0CLT10001CLIENT AU COMPTANT2984982456498383MAARIF331.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-27
92880147008773232024-02-2908:34:11Carte prépayéeNaN8.08.00.0CLT10001CLIENT AU COMPTANT3417943491637353EZ-ZAARATE141.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-02-29
ID_RèglementID_OperationDate_RèglementHeure_RèglementPaiementRéférenceMontant_RglMontant_VerséMontant_RstID_ClientClientID_CartePPBénéficiaire_CPPSolde_CPPID_RestaurantRestaurantID_UserPrenom UserRoleStatutPointageDate_Sys
152123877489061057052024-09-0407:43:20Carte prépayéeNaN19.019.00.0CLT10001CLIENT AU COMPTANT9632819172214156DOUDOUH1903.30RST10014Epicerie - RabatUSR10029MoncefCaissierValidéNaN2024-09-04
152133878979061057312024-09-0409:45:12Carte prépayéeNaN4.04.00.0CLT10001CLIENT AU COMPTANT9697966821673656CHERKAOUI13.50RST10014Epicerie - RabatUSR10029MoncefCaissierValidéNaN2024-09-04
152143879127009157032024-09-0409:58:58Carte prépayéeNaN13.013.00.0CLT10001CLIENT AU COMPTANT9648732328734427HANAFI79.50RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-09-04
152153879149061057352024-09-0410:00:14Carte prépayéeNaN19.019.00.0CLT10001CLIENT AU COMPTANT3657743229762819JENNANE74.50RST10014Epicerie - RabatUSR10029MoncefCaissierValidéNaN2024-09-04
152163879307009157092024-09-0410:10:07Carte prépayéeNaN4.04.00.0CLT10001CLIENT AU COMPTANT9647665548393481HANINE Mohamed34.50RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-09-04
152173879327009157102024-09-0410:10:28Carte prépayéeNaN7.07.00.0CLT10001CLIENT AU COMPTANT3314783631225925AQEL1453.55RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-09-04
152183879447009157162024-09-0410:20:13Carte prépayéeNaN35.035.00.0CLT10001CLIENT AU COMPTANT9166242347522725AIT HAJJI6000.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-09-04
152193879917009157302024-09-0410:52:03Carte prépayéeNaN54.054.00.0CLT10001CLIENT AU COMPTANT3391323398681767ZAGHBACH59.00RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-09-04
152203880149061057682024-09-0411:16:27Carte prépayéeNaN5.05.00.0CLT10001CLIENT AU COMPTANT9364826446274539ZAHID66.00RST10014Epicerie - RabatUSR10029MoncefCaissierValidéNaN2024-09-04
152213880347009157382024-09-0411:25:34Carte prépayéeNaN8.08.00.0CLT10001CLIENT AU COMPTANT9934258673758229REMMAL36.50RST10005Café - Boulangerie - Pâtisserie - RabatUSR10004HamzaCaissierValidéNaN2024-09-04